In this study, we use English and Bangla languages and three emotional speech sets for evaluating the independency of language in Speech Emotion Recognition. The Toronto Emotional Speech Set (TESS) [1]} is used for the English Language, and the Bangla and English Speech set is developed by native Bengali speakers in accordance with the TESS [2]}. The development process is thoroughly discussed in Section . Following this in Section  and Section REF  we construct and analyze a feature set respectively by carefully selecting language-independent features such as pitch mean, pitch median, pitch standard deviation and intensity [3]}, [4]}, [5]}, [6]}, [7]}, followed by adopting a Support Vector Machine Classifier (SVM) [8]} for recognizing emotional states in Section . Additionally, we employed traditional classifiers for SER, such as HMM(Hidden Markov Model), GMM (Gaussian Mixture Model), Artificial Neural Networks (ANN), and k-Nearest Neighbour (kNN) [9]}. However, the emotion recognition rate for these classifiers was recorded to be lower than SVM. Since our objective is not to increase the performance of our SER system rather to analyse the nuances in Bangla and English languages, we only selected SVM for conducting our research. We used six emotional states such as happy, angry, neutral, sad, disgust, and fear [3]}, [11]}, [12]}, [5]} for our speech emotion recognition system. Finally, investigating the language independence of Speech Emotion Recognition is carried out in Section  and its scope and future possibilities are discussed in Section .
